CN117629131A - Precision evaluation method and system for optical fiber shape sensing - Google Patents
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Abstract
The invention provides a precision evaluation method and a system for optical fiber shape sensing, comprising the steps of reconstructing a sensor and obtaining a reconstructed sensor curve; dividing a curve of the sensor into a plurality of sensing sections, and respectively inspecting error distribution of the sensing sections; acquiring error parameters for evaluating the overall performance of the sensor; error parameters of sensors of different specifications are normalized to the same dimension to laterally compare reconstruction effects of the sensors of different specifications. Compared with the prior art, the method and the device can find the precision change trend of each segment in the local error investigation, give the segment weighted total error with investigation bias to the sensing segment in the global error investigation, comprehensively evaluate the optical fiber shape sensing precision according to the global and local reconstruction curve error information, realize the transverse evaluation of the shape reconstruction effect of the sensors with different specifications, and assist researchers in balancing the precision improvement brought by the sensing points and the cost improvement of the sensors so as to meet the investigation requirements of differentiation under different scenes.
Description
Technical Field
The invention relates to the technical field of optical fiber shape sensing, in particular to an accuracy assessment method and an accuracy assessment system for optical fiber shape sensing.
Background
The optical fiber shape sensing technology is an emerging technology in the shape sensing field in recent years, and becomes an ideal solution in the fields of civil facilities, precision machinery, aerospace engineering, biomedical treatment, medicine and the like based on the advantages of small volume, insulation, high pressure resistance, high temperature resistance, corrosion resistance, high biocompatibility and the like of the optical fiber. The optical fiber shape sensing technology can be seen as an organic combination of optical fiber strain measurement technology, optical fiber sensor configuration design and advanced three-dimensional reconstruction algorithm. The optical fiber strain measurement technology is used for measuring the strain response of an optical fiber sensor integrated in the object to be measured under deformation, and the demodulated strain information is substituted into a three-dimensional shape reconstruction algorithm for updating iteration, so that the space coordinate information of the whole sensor can be recovered, and the space posture of the object to be measured is obtained. For the optical fiber shape sensing technology, the shape sensing precision is a key problem of whether the optical fiber shape sensing technology can be applied to practical engineering.
However, the accuracy evaluation method for optical fiber shape sensing in the prior art has drawbacks, such as:
the existing precision evaluation method lacks detailed evaluation of the interesting position, sometimes cannot meet engineering requirements, and because of the difference of sensor specifications proposed by researchers, transverse comparison among sensors can only be completed through a certain parameter, which introduces serious evaluation deviation. Meanwhile, the existing precision evaluation method is not suitable for the data-driven optical fiber shape sensing method proposed by Sefati S et al, IEEE Sensors Journal (2020), 21 (3): 3066-3076.
Khan F et al Sensors and Actuators A:physical (2021), 317:112442 used an end error based accuracy assessment method, in three-dimensional reconstruction algorithm research, since the reconstruction error accumulation is caused by the point-by-point recursive updating method in the optical fiber shape sensing reconstruction algorithm, the end of the reconstruction curve is usually the maximum point of the error accumulation, and researchers evaluate the sensing accuracy by examining the deviation of the end point of the reconstruction curve from the end point of the original curve, and the following problems need to be solved in the assessment method:
(1) The accuracy evaluation method based on the end error ignores the space information under the longer distance from the reconstruction starting point to the middle of the end, and in some cases, the end error of the reconstruction curve is not the maximum point of the reconstruction curve error, and under the limiting environment, the situation that the error in the middle section of the reconstruction curve is larger than the reconstruction accuracy requirement can occur, so that the subsequent reconstruction calculation is invalidated.
(2) The precision evaluation method based on the tail end error is proposed in the research of a three-dimensional reconstruction algorithm due to error accumulation caused by a point-by-point recursive updating mode in the reconstruction algorithm of the optical fiber shape sensing, and the evaluation method can fail for the optical fiber shape sensing method based on the data driving proposed by Sefati S et al IEEE Sensors Journal (2020), 21 (3): 3066-3076.
S et al, international journal of computer assisted radiology and surgery (2019), 14 (12) 2137-2145 uses an average error evaluation method and a maximum error evaluation method which take global information into account, the basic idea of the method is simpler, namely, calculating the deviation values of each point of a reconstruction curve and averaging according to the reconstruction points to obtain average deviation, and searching the maximum deviation point in all reconstruction data points, and integrally evaluating the shape sensing precision through the average deviation and the maximum deviation; the overall average deviation and the maximum deviation value of the reconstruction curve are used as the accuracy evaluation basis, and the evaluation method has the following problems to be solved:
(1) The average error used in this accuracy assessment method bisects the global error over the entire length of the sensor, resulting in an inaccurate investigation of the distribution of local error information. When an extreme situation such as the error distribution of the reconstruction curve is centrosymmetric, the average errors calculated by the two are the same, resulting in distortion of the accuracy assessment.
(2) The maximum error used by the precision evaluation method is the maximum error point in the whole space, but the space error distribution of the whole sensor is not deeply studied, and when a plurality of intervals with larger loss occur, a large amount of space information is ignored.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides an accuracy assessment method and an accuracy assessment system for optical fiber shape sensing, and the specific technical scheme is as follows:
a precision evaluation method for optical fiber shape sensing, comprising:
reconstructing a sensor and acquiring a reconstructed curve of the sensor;
dividing the curve of the sensor into a plurality of sensing sections, and respectively inspecting the error distribution of the sensing sections;
acquiring error parameters for evaluating the overall performance of the sensor;
normalizing the error parameters of the sensors of different specifications to the same dimension to laterally compare the reconstruction effects of the sensors of different specifications.
In a specific embodiment, the "the reconstruction effect of the sensor of different specifications in the lateral direction" specifically includes:
acquiring a plurality of sensors with different specifications, and respectively calculating average sensing lengths of the sensors with different specifications;
comparing the average sensing length of each sensor, finding the maximum value of the average sensing length, carrying out normalization processing on the rest average sensing lengths except the maximum value, and respectively calculating the ratio between the rest average sensing lengths and the maximum value of the average sensing length to obtain a series of normalization proportional coefficients;
multiplying the normalized proportion coefficient with the corresponding weighted error to obtain a normalized weighted error, and analyzing and comparing the normalized weighted error.
In a specific embodiment, the "examining the error distribution of the sensor segments" specifically includes:
and calculating the average error of the sensing segments based on the number of sensing points in each sensing segment, and analyzing and comparing the average error of the sensing segments one by one to obtain the error distribution of each part of the sensor.
In a specific embodiment, the "obtaining an error parameter for evaluating the overall performance of the sensor" specifically includes:
and setting weights for different sensing segments according to the error distribution and the reconstruction effect, and carrying out weighted summation on average errors of the sensing segments to obtain error parameters for evaluating the overall performance of the sensor.
In a specific embodiment, the calculation manner of "respectively examining the error distribution of the sensing section" specifically includes:
wherein,representing the error distribution of the sense segments, and (2)>Representing the number of sensing points in said sensing section,/->Three-dimensional coordinates representing the end of the reconstruction curve of the sensor segment, < >>And representing the three-dimensional coordinates of the tail end of the reference curve of the sensing section, wherein i represents the i-th sensing section after segmentation.
In a specific embodiment, the calculation method for obtaining the error parameter for evaluating the overall performance of the sensor specifically includes:
wherein e weighti Representing an error parameter, W, for evaluating the overall performance of the sensor 1 A weight value representing a sensor segment error set according to a use environment, and n represents the number of divided segments of the sensor.
In a specific embodiment, the calculation formula of "calculating the average sensing lengths of the sensors of different specifications" specifically includes:
wherein L is avgi Representing average sensing length, L, of the sensors of different specifications i Representing the lengths of the sensors of different specifications, n i Representing the number of sensing points of the sensors of different specifications.
In a specific embodiment, the calculation formula of the "normalized scaling factor" specifically includes:
wherein K is im Represents a normalized scale factor, L avgm Represents the maximum value, L, of the average sensing length of the sensor avgi Representing the remaining average sensing length of the sensor excluding the maximum value.
In a specific embodiment, the calculation formula of the "normalized weighted error" specifically includes:
e normi ＝k im ·e weighti ；
wherein e normi Represents the normalized weighted error, K im Represents a normalized scaling factor, e weighti Representing an error parameter that evaluates the overall performance of the sensor.
In a specific embodiment, there is also provided a precision evaluation system for optical fiber shape sensing, comprising:
the reconstruction unit is used for reconstructing the sensor and acquiring a reconstructed curve of the sensor;
the inspection unit is used for dividing the curve of the sensor into a plurality of sensing sections and inspecting the error distribution of the sensing sections respectively;
an acquisition unit for acquiring error parameters for evaluating the overall performance of the sensor;
and the normalization unit is used for normalizing the error parameters of the sensors with different specifications to the same dimension so as to transversely compare the reconstruction effects of the sensors with different specifications.
Compared with the prior art, the invention has the following beneficial effects:
the precision evaluation method and the precision evaluation system for the optical fiber shape sensing can be used for finding the precision change trend of each segment in the local error investigation, giving the segment weighted total error with investigation bias to the sensing segment in the global error investigation, comprehensively evaluating the optical fiber shape sensing precision by global and local reconstruction curve error information, realizing the transverse evaluation of the shape reconstruction effect of the sensors with different specifications, assisting researchers in balancing the precision improvement brought by the sensing points and the cost improvement of the sensors, reducing the evaluation bias, meeting the investigation requirements of differentiation under different scenes, avoiding losing the spatial position information and enhancing the universality.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of evaluation parameter calculation of a precision evaluation method for optical fiber shape sensing in an embodiment;
FIG. 2 is a flow chart of a method of normalizing sensor segment length in an embodiment;
FIG. 3 is a schematic diagram of a different gauge sensor in an embodiment;
FIG. 4 is a schematic diagram of an accuracy assessment system for fiber optic shape sensing in an embodiment.
Detailed Description
Hereinafter, various embodiments of the present invention will be described more fully. The invention is capable of various embodiments and of modifications and variations therein. However, it should be understood that: there is no intention to limit the various embodiments of the invention to the specific embodiments disclosed herein, but rather the invention is to be understood to cover all modifications, equivalents, and/or alternatives falling within the spirit and scope of the various embodiments of the invention.
Hereinafter, the terms "comprises" or "comprising" as may be used in various embodiments of the present invention indicate the presence of the disclosed functions, operations or elements, and are not limiting of the addition of one or more functions, operations or elements. Furthermore, as used in various embodiments of the invention, the terms "comprises," "comprising," and their cognate terms are intended to refer to a particular feature, number, step, operation, element, component, or combination of the foregoing, and should not be interpreted as first excluding the existence of or increasing likelihood of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing.
In various embodiments of the invention, the expression "or" at least one of a or/and B "includes any or all combinations of the words listed simultaneously. For example, the expression "a or B" or "at least one of a or/and B" may include a, may include B or may include both a and B.
Expressions (such as "first", "second", etc.) used in the various embodiments of the invention may modify various constituent elements in the various embodiments, but the respective constituent elements may not be limited. For example, the above description does not limit the order and/or importance of the elements. The above description is only intended to distinguish one element from another element. For example, the first user device and the second user device indicate different user devices, although both are user devices. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of various embodiments of the present invention.
It should be noted that: in the present invention, unless explicitly specified and defined otherwise, terms such as "mounted," "connected," "secured," and the like are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium; may be a communication between the interiors of the two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the present invention, it should be understood by those of ordinary skill in the art that the terms indicating an orientation or a positional relationship are based on the orientation or positional relationship shown in the drawings, and are merely for convenience of description and simplicity of description, not to indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the invention.
The terminology used in the various embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the various embodiments of the invention. As used herein, the singular is intended to include the plural as well, unless the context clearly indicates otherwise. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the invention belong. The terms (such as those defined in commonly used dictionaries) will be interpreted as having a meaning that is the same as the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein in connection with the various embodiments of the invention.
Examples
As shown in fig. 1 to 3, the present embodiment provides a precision evaluation method for optical fiber shape sensing, including:
reconstructing the sensor and acquiring a curve of the reconstructed sensor; dividing a curve of the sensor into a plurality of sensing sections, and respectively inspecting error distribution of the sensing sections; acquiring error parameters for evaluating the overall performance of the sensor; error parameters of sensors of different specifications are normalized to the same dimension to laterally compare reconstruction effects of the sensors of different specifications.
The method and the device can solve the problems that the existing precision evaluation method loses space position information and is poor in universality, can realize transverse comparison of the sensing performance of the sensors with different specifications through the normalized sensing length, and can assist researchers in balancing precision improvement brought by sensing points and cost improvement of the sensors. Preferably, the embodiment can find out the precision change trend of each segment in the local error investigation, give out the segment weighted total error with investigation bias to the sensing segment in the global error investigation, can comprehensively evaluate the optical fiber shape sensing precision by global and local reconstruction curve error information, can realize the transverse evaluation of the shape reconstruction effect of the sensors with different specifications, can assist researchers in balancing the precision improvement brought by the sensing points and the cost improvement of the sensors, reduces the evaluation bias, meets the differentiated investigation requirements under different scenes, avoids losing the spatial position information and enhances the universality.
Optionally, the embodiment can integrate global and local reconstruction curve error information, and simultaneously can set different weights for different segments to realize flexible investigation bias; then, a method for normalizing the length of the sensing section can be developed on the basis, and error parameters of the sensors with different specifications are normalized to the same dimension, so that the transverse comparison of the reconstruction effect of the sensors with different specifications is realized. The embodiment has the potential of assisting researchers in finding out balance points suitable for the researchers in precision improvement and sensor cost improvement brought by the sensing points so as to meet differentiated investigation requirements in different scenes.
In order to realize the transverse comparison of the reconstruction effect of the sensors with different specifications, a method for normalizing the length of the sensing section is further developed in the method, error parameters of the sensors with different specifications are normalized to the same dimension, and a flow chart for realizing the method is shown in fig. 2.
Specifically, in the present embodiment, the "reconstruction effect of the sensors of different specifications in the lateral direction" specifically includes:
obtaining a plurality of sensors with different specifications, and assuming that k sensors with different specifications are provided, the lengths of the sensors are L respectively 1 ,L 2 ,…,L k And respectively have n 1 ,n 2 ,…,n k The average sensing length of the sensors with the specifications is calculated by the sensing points; comparing the average sensing length of each sensor to find the maximum value L of the average sensing length avgm And carrying out normalization processing on other average sensing lengths, and respectively calculating the ratio of the average sensing length to the maximum average sensing length, namely: normalizing the rest average sensing length except the maximum value, and calculating the ratio of the rest average sensing length to the maximum value of the average sensing length to obtain a series of normalized proportional coefficients k 1m ,k 2m ,...,k km The method comprises the steps of carrying out a first treatment on the surface of the Multiplying the normalized proportion coefficient by the corresponding weighted error to obtain a normalized weighted error e norm By weighting the normalized error e norm And (3) carrying out analysis and comparison, so that the reconstruction effect of the sensors with different specifications can be compared laterally and partially.
In this embodiment, "respectively inspecting the error distribution of the sensing segments" specifically includes:
based on the number of sensing points in each sensing section, calculating the average error of each sensing section, and analyzing and comparing the average errors of a plurality of sensing sections of the reconstruction curve one by one to obtain the error distribution of each part of the sensor.
In this embodiment, "obtaining an error parameter for evaluating the overall performance of the sensor" specifically includes:
according to the rough error distribution and reconstruction effect in the actual application sceneThe weight is set for different sensing segments, and then the average error of the sensing segments is weighted and summed, so that an error parameter e for evaluating the overall performance of the sensor can be obtained weight 。
Specifically, an evaluation parameter calculation flow chart of an accuracy evaluation method for optical fiber shape sensing is shown in fig. 1, and the evaluation method comprises the steps of firstly dividing a reconstructed sensor curve into a plurality of sensing segments by a segmentation method, and setting a certain weight for each segment to adjust the importance degree of each position in evaluation; and then, normalizing the sensor according to the length of the sensor and the number of the sensor points to obtain a normalized error value, and comparing the normalized error value. The specific steps are as follows:
(1) Dividing the reconstruction curve into a plurality of sensing segments S 1 ,S 2 ,…,S j The sum of the lengths of the sensing sections is the total length L of the sensor, and error conditions are separately inspected for the sensing sections; (2) Based on the number of sensing points in each sensing section, calculating the average error of each sensing section, and analyzing and comparing the errors of a plurality of sensing sections of the reconstruction curve one by one to obtain the error distribution of each part of the sensor; (3) The weight is set for different segments according to the rough error distribution and reconstruction effect in the actual application scene, and then the average error of each sensing segment is weighted and summed, thus obtaining a parameter e for evaluating the overall performance of the sensor weight 。
According to the embodiment, the sensor is segmented and different weights are respectively set, the precision change trend of each segment can be seen in the local error investigation, and the segmentation weighted total error with the investigation deflection of the sensor segment is given in the global error investigation, so that the overall global and local reconstruction curve error information is comprehensively evaluated on the optical fiber shape sensing precision. The method can normalize the lengths of the sensing sections of different sensors on the basis of the segmentation weighted errors, so that error parameters of the sensors with different specifications are normalized to the same dimension, and the shape reconstruction effect transverse evaluation of the sensors with different specifications is realized.
In this embodiment, the calculation method for "respectively inspecting the error distribution of the sensing section" specifically includes:
wherein,representing the error distribution of the sensor segments, +.>Representing the number of sensing points in the sensing section, < >>Three-dimensional coordinates representing the end of the reconstruction curve of the sensor segment,/->Representing the three-dimensional coordinates of the end of the reference curve of the sensor segment, i representing the i-th sensor segment after segmentation.
In this embodiment, the calculation method for "obtaining the error parameter for evaluating the overall performance of the sensor" specifically includes:
wherein e weighti Representing an error parameter for evaluating the overall performance of the sensor, W 1 A weight value indicating a sensor segment error set according to a use environment, and n indicating the number of segments of the sensor.
In this embodiment, the calculation formula for "calculating the average sensing length of the sensors with different specifications" specifically includes:
wherein L is avgi Representing average sensing length, L, of sensors of different specifications i Indicating the lengths of the sensors of different specifications, n i Representing different specifications of transmissionThe number of sensor points of the sensor.
In this embodiment, the calculation formula of the "normalized scaling factor" specifically includes:
wherein K is im Represents a normalized scale factor, L avgm Representing the maximum value, L, of the average sensing length of the sensor avgi Representing the remaining average sensing length of the sensor excluding the maximum value.
In this embodiment, the calculation formula of the "normalized weighted error" specifically includes:
e normi ＝k im ·e weighti ；
wherein e normi Represents the normalized weighted error, K im Represents a normalized scaling factor, e weighti Representing an error parameter that evaluates the overall performance of the sensor.
Optionally, in the method for evaluating the precision of the optical fiber shape sensing in the embodiment, the sensor is weighted in a segmentation way, so that the influence of the sensing section or the interest sensing section with great influence on the reconstruction precision on the sensing precision can be considered in a key way according to the user's wish, and the sensor shape reconstruction effect evaluation of fully integrating the space information is realized.
Optionally, the accuracy evaluation method for optical fiber shape sensing in this embodiment may further implement transverse comparison of shape reconstruction performance of sensors with different specifications by using a normalization method, which is helpful for improving accuracy and cost caused by improving the number of sensing points of the user balance sensor, which is an important reference information for commercialization of optical fiber sensing technology.
As shown in fig. 4, in the present embodiment, there is also provided a precision evaluation system for optical fiber shape sensing, including:
the reconstruction unit is used for reconstructing the sensor and acquiring a curve of the reconstructed sensor;
the inspection unit is used for dividing the curve of the sensor into a plurality of sensing sections and inspecting the error distribution of the sensing sections respectively;
an acquisition unit for acquiring error parameters for evaluating the overall performance of the sensor;
and the normalization unit is used for normalizing error parameters of the sensors with different specifications to the same dimension so as to transversely compare the reconstruction effects of the sensors with different specifications.
Compared with the prior art, the precision evaluation method and the precision evaluation system for the optical fiber shape sensing can be used for finding the precision change trend of each segment in the local error investigation, giving the segment weighted total error for investigating the deviation of the sensing segment in the global error investigation, comprehensively evaluating the optical fiber shape sensing precision through global and local reconstruction curve error information, realizing the transverse evaluation of the shape reconstruction effect of the sensors with different specifications, assisting researchers in balancing the precision improvement brought by the sensing points and the cost improvement of the sensors, reducing evaluation deviation, meeting the differentiated investigation requirements under different scenes, avoiding losing the space position information and enhancing the universality.
Those skilled in the art will appreciate that the drawing is merely a schematic illustration of a preferred implementation scenario and that the modules or flows in the drawing are not necessarily required to practice the invention.
Those skilled in the art will appreciate that modules in an apparatus in an implementation scenario may be distributed in an apparatus in an implementation scenario according to an implementation scenario description, or that corresponding changes may be located in one or more apparatuses different from the implementation scenario. The modules of the implementation scenario may be combined into one module, or may be further split into a plurality of sub-modules.
The above-mentioned inventive sequence numbers are merely for description and do not represent advantages or disadvantages of the implementation scenario.
The foregoing disclosure is merely illustrative of some embodiments of the invention, and the invention is not limited thereto, as modifications may be made by those skilled in the art without departing from the scope of the invention.
Claims (10)
1. A precision evaluation method for optical fiber shape sensing, comprising:
reconstructing a sensor and acquiring a reconstructed curve of the sensor;
dividing the curve of the sensor into a plurality of sensing sections, and respectively inspecting the error distribution of the sensing sections;
acquiring error parameters for evaluating the overall performance of the sensor;
normalizing the error parameters of the sensors of different specifications to the same dimension to laterally compare the reconstruction effects of the sensors of different specifications.
2. The method for evaluating the accuracy of optical fiber shape sensing according to claim 1, wherein the "the reconstruction effect of the sensor of the different specifications is compared laterally" specifically includes:
acquiring a plurality of sensors with different specifications, and respectively calculating average sensing lengths of the sensors with different specifications;
comparing the average sensing length of each sensor, finding the maximum value of the average sensing length, carrying out normalization processing on the rest average sensing lengths except the maximum value, and respectively calculating the ratio between the rest average sensing lengths and the maximum value of the average sensing length to obtain a series of normalization proportional coefficients;
multiplying the normalized proportion coefficient with the corresponding weighted error to obtain a normalized weighted error, and analyzing and comparing the normalized weighted error.
3. The precision evaluation method for optical fiber shape sensing according to claim 1, wherein the "examining error distribution of the sensing segments, respectively" specifically comprises:
and calculating the average error of the sensing segments based on the number of sensing points in each sensing segment, and analyzing and comparing the average error of the sensing segments one by one to obtain the error distribution of each part of the sensor.
4. The method for evaluating the accuracy of optical fiber shape sensing according to claim 1, wherein the step of acquiring an error parameter for evaluating the overall performance of the sensor comprises:
and setting weights for different sensing segments according to the error distribution and the reconstruction effect, and carrying out weighted summation on average errors of the sensing segments to obtain error parameters for evaluating the overall performance of the sensor.
5. The method for evaluating the accuracy of optical fiber shape sensing according to claim 1, wherein the calculation method for "examining the error distribution of the sensing section respectively" specifically includes:
wherein,representing the error distribution of the sense segments, and (2)>Representing the number of sensing points in said sensing section,/->Representing the three-dimensional coordinates of the end of the reconstruction curve of the sensor segment, r i gt And representing the three-dimensional coordinates of the tail end of the reference curve of the sensing section, wherein i represents the i-th sensing section after segmentation.
6. The method for evaluating the accuracy of optical fiber shape sensing according to claim 1, wherein the calculation method for obtaining the error parameter for evaluating the overall performance of the sensor specifically comprises:
wherein e weighti Representing an error parameter, W, for evaluating the overall performance of the sensor 1 A weight value representing a sensor segment error set according to a use environment, and n represents the number of divided segments of the sensor.
7. The accuracy evaluation method for optical fiber shape sensing according to claim 2, wherein the calculation formula of "calculating average sensing lengths of the sensors of different specifications, respectively" specifically includes:
wherein L is avgi Representing average sensing length, L, of the sensors of different specifications i Representing the lengths of the sensors of different specifications, n i Representing the number of sensing points of the sensors of different specifications.
8. The method for evaluating the accuracy of optical fiber shape sensing according to claim 2, wherein the calculation formula of the "normalized scaling factor" specifically includes:
wherein K is im Represents a normalized scale factor, L avgm Represents the maximum value, L, of the average sensing length of the sensor avgi Representing the remaining average sensing length of the sensor excluding the maximum value.
9. The method for evaluating the accuracy of optical fiber shape sensing according to claim 2, wherein the calculation formula of the "normalized weighted error" specifically includes:
e notmi ＝k im ·e weighti ；
wherein e normi Represents the normalized weighted error, K im Represents a normalized scaling factor, e weighti Representing an error parameter that evaluates the overall performance of the sensor.
10. A precision evaluation system for optical fiber shape sensing, comprising:
the reconstruction unit is used for reconstructing the sensor and acquiring a reconstructed curve of the sensor;
the inspection unit is used for dividing the curve of the sensor into a plurality of sensing sections and inspecting the error distribution of the sensing sections respectively;
an acquisition unit for acquiring error parameters for evaluating the overall performance of the sensor;
and the normalization unit is used for normalizing the error parameters of the sensors with different specifications to the same dimension so as to transversely compare the reconstruction effects of the sensors with different specifications.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210983368.9A CN117629131A (en) | 2022-08-16 | 2022-08-16 | Precision evaluation method and system for optical fiber shape sensing |
| PCT/CN2022/137066 WO2024036824A1 (en) | 2022-08-16 | 2022-12-06 | Precision evaluation method and system for optical-fiber shape sensing |
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| JP2015181643A (en) * | 2014-03-24 | 2015-10-22 | オリンパス株式会社 | Curved shape estimation system, tubular insert system, and method for estimating curved shape of curved member |
| CN103994784A (en) * | 2014-05-26 | 2014-08-20 | 天津大学 | Distributed optical fiber sensing positioning method based on zero crossing point analysis |
| WO2017030648A2 (en) * | 2015-06-17 | 2017-02-23 | The Charles Stark Draper Laboratory, Inc | Systems and methods for determining shape and/or position |
| CN110657753B (en) * | 2019-09-02 | 2023-08-04 | 山东大学 | Sensing system and method for monitoring deformation field of linear engineering |
| CN113190985A (en) * | 2021-04-21 | 2021-07-30 | 北京农业信息技术研究中心 | Method and system for analyzing data errors in layered heterogeneous region |
| CN114154119B (en) * | 2021-11-30 | 2023-03-31 | 哈尔滨工程大学 | Adaptive curve reconstruction method for array fiber grating shape sensor |
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